In [1]:
import pandas as pd 
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt
In [2]:
netflix=pd.read_csv("netflix.csv")
In [3]:
netflix.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8790 entries, 0 to 8789
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   show_id       8790 non-null   object 
 1   type          8790 non-null   object 
 2   title         8790 non-null   object 
 3   director      8790 non-null   object 
 4   country       8790 non-null   object 
 5   date_added    8790 non-null   object 
 6   release_year  8790 non-null   int64  
 7   score         8405 non-null   float64
 8   rating        8790 non-null   object 
 9   duration      8790 non-null   object 
 10  listed_in     8790 non-null   object 
dtypes: float64(1), int64(1), object(9)
memory usage: 755.5+ KB
In [4]:
netflix.head()
Out[4]:
show_id type title director country date_added release_year score rating duration listed_in
0 s1 Movie Dick Johnson Is Dead Kirsten Johnson United States 2021-09-25 2020 7.4 PG-13 90 min Documentaries
1 s3 TV Show Ganglands Julien Leclercq France 2021-09-24 2021 7.3 TV-MA 1 Season Crime TV Shows, International TV Shows, TV Act...
2 s6 TV Show Midnight Mass Mike Flanagan United States 2021-09-24 2021 7.6 TV-MA 1 Season TV Dramas, TV Horror, TV Mysteries
3 s14 Movie Confessions of an Invisible Girl Bruno Garotti Brazil 2021-09-22 2021 6.9 TV-PG 91 min Children & Family Movies, Comedies
4 s8 Movie Sankofa Haile Gerima United States 2021-09-24 1993 6.0 TV-MA 125 min Dramas, Independent Movies, International Movies
In [5]:
netflix.columns
Out[5]:
Index(['show_id', 'type', 'title', 'director', 'country', 'date_added',
       'release_year', 'score', 'rating', 'duration', 'listed_in'],
      dtype='object')
In [6]:
netflix.describe()
Out[6]:
release_year score
count 8790.000000 8405.000000
mean 2014.183163 6.184533
std 8.825466 2.030113
min 1925.000000 0.000000
25% 2013.000000 5.800000
50% 2017.000000 6.600000
75% 2019.000000 7.400000
max 2021.000000 10.000000
In [7]:
# Checking if there are any duplicates
netflix.duplicated().value_counts()
Out[7]:
False    8790
dtype: int64
In [8]:
def mostCommonDuration(data=netflix,nbr=10):
    
    data=netflix.groupby('duration').count().sort_values(by='show_id',ascending=False)
    data=data["show_id"]
    list(data[:10])
    labels=data.index[:nbr]
    values=list(data[:nbr])
    plt.pie(values,labels=labels, autopct='%1.1f%%', startangle=90)
    plt.title("Most Common Duration")
    plt.show
In [9]:
mostCommonDuration(data=netflix,nbr=10)
In [10]:
def mostTypeOfShow(data=netflix):
    data=netflix.groupby(by="type").count()
    data=data["show_id"]
    labels=data.index
    values=list(data)
    plt.pie(values,labels=labels, autopct='%1.1f%%', startangle=90)
    plt.title("Most common type of show")
    plt.show
In [11]:
mostTypeOfShow()
In [12]:
#cleanning of themes
df_clean=netflix
In [13]:
df_clean['Genre1'] = 0
df_clean['Genre2'] = 0
df_clean['Genre3'] = 0
temp_cat = df_clean.listed_in.str.split(',')
i=0
for i in range (8790):
    t_cat = temp_cat[i]
    if len(t_cat) == 1:
        df_clean['Genre1'][i] = temp_cat[i][0]
        df_clean['Genre2'][i] = 0
        df_clean['Genre3'][i] = 0
    if len(t_cat) == 2:
        df_clean['Genre1'][i] = temp_cat[i][0]
        df_clean['Genre2'][i] = temp_cat[i][1]
        df_clean['Genre3'][i] = 0
    if len(t_cat) == 3:
        df_clean['Genre1'][i] = temp_cat[i][0]
        df_clean['Genre2'][i] = temp_cat[i][1]
        df_clean['Genre3'][i] = temp_cat[i][2]

C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\1274348222.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_clean['Genre1'][i] = temp_cat[i][0]
C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\1274348222.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_clean['Genre2'][i] = 0
C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\1274348222.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_clean['Genre3'][i] = 0
C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\1274348222.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_clean['Genre2'][i] = temp_cat[i][1]
C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\1274348222.py:19: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_clean['Genre3'][i] = temp_cat[i][2]
In [14]:
def mostCommonGenre1(data=netflix,nbr=5):
    data=df_clean.groupby(by="Genre1").count().sort_values(by='show_id',ascending=False)
    data=data["show_id"]
    labels=data.index[:nbr]
    values=list(data[:nbr])
    plt.pie(values,labels=labels, autopct='%1.1f%%', startangle=90)
    plt.title("Most common Genres")
    plt.show
In [15]:
mostCommonGenre1()
In [36]:
def meanScoreOfmostPopularGenre(df=netflix,nbr=10):
    data=df.groupby(by="Genre1").mean().sort_values(by='score',ascending=False)
    data=data["score"]
    data=data.map(lambda x:str(x)[:4])
    data=data.map(lambda x: float(x))
    labels=data.index[:nbr]
    sizes=data[:nbr]
    plt.bar(labels,sizes,width=0.3)
    plt.xticks(rotation=90)
    for i in range(len(sizes)):
        plt.annotate(str(sizes[i]), xy=(labels[i],sizes[i]), ha='center', va='bottom')
    plt.show()
    
meanScoreOfmostPopularGenre()

C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\729589146.py:2: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
In [17]:
score_by_country=netflix.groupby(by="country").mean().sort_values(by='score',ascending=False)
score_by_country=score_by_country["score"]
nbr_movie_by_country=netflix.groupby(by="country").count().sort_values(by='score',ascending=False)
nbr_movie_by_country=nbr_movie_by_country["show_id"]
nbr_movie_by_country=pd.DataFrame(nbr_movie_by_country)
nbr_movie_by_country["score"]=list(score_by_country)
data=nbr_movie_by_country
data.columns=["nbr_show","score"]

C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\2476004678.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  score_by_country=netflix.groupby(by="country").mean().sort_values(by='score',ascending=False)
In [18]:
data["CountryName"]=data.index
data.index=[i for i in range(len(data))]
df=data
test=pd.DataFrame(df["CountryName"])
test
Out[18]:
CountryName
0 United States
1 India
2 United Kingdom
3 Pakistan
4 Canada
... ...
81 Soviet Union
82 Paraguay
83 Puerto Rico
84 Somalia
85 Zimbabwe

86 rows × 1 columns

In [19]:
test["nbr_show"]=data["nbr_show"]
test
Out[19]:
CountryName nbr_show
0 United States 3240
1 India 1057
2 United Kingdom 638
3 Pakistan 421
4 Canada 271
... ... ...
81 Soviet Union 1
82 Paraguay 1
83 Puerto Rico 1
84 Somalia 1
85 Zimbabwe 1

86 rows × 2 columns

In [20]:
#function to convert to alpah2 country codes and continents
from pycountry_convert import country_alpha2_to_continent_code, country_name_to_country_alpha2
def get_continent(col):
    try:
        cn_a2_code =  country_name_to_country_alpha2(col)
    except:
        cn_a2_code = 'Unknown' 
    try:
        cn_continent = country_alpha2_to_continent_code(cn_a2_code)
    except:
        cn_continent = 'Unknown' 
    return (cn_a2_code, cn_continent)
In [21]:
Country=[]
Continent=[]
codes=[]
for i in range(len(test)):
    country_name,country_continent=get_continent(test["CountryName"][i])
    codes.append((country_name,country_continent))
    Country.append(country_name)
    Continent.append(country_continent)
test["Country"]=Country
test["Continent"]=Continent
test["codes"]=codes
test
Out[21]:
CountryName nbr_show Country Continent codes
0 United States 3240 US NA (US, NA)
1 India 1057 IN AS (IN, AS)
2 United Kingdom 638 GB EU (GB, EU)
3 Pakistan 421 PK AS (PK, AS)
4 Canada 271 CA NA (CA, NA)
... ... ... ... ... ...
81 Soviet Union 1 Unknown Unknown (Unknown, Unknown)
82 Paraguay 1 PY SA (PY, SA)
83 Puerto Rico 1 PR NA (PR, NA)
84 Somalia 1 SO AF (SO, AF)
85 Zimbabwe 1 ZW AF (ZW, AF)

86 rows × 5 columns

In [22]:
#function to get longitude and latitude data from country name
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent='your unique UA')
def geolocate(country):
    try:
        # Geolocate the center of the country
        
        loc = geolocator.geocode(country)
        # And return latitude and longitude
        return (loc.latitude, loc.longitude)
    except:
        # Return missing value
        return np.nan
In [23]:
Geolocate=[]
Latitude=[]
Longitude=[]
for i in range(len(test)):
    cn_latitude,cn_longitude=geolocate(test["CountryName"][i])
    Geolocate.append((cn_latitude,cn_longitude))
    Latitude.append(cn_latitude)
    Longitude.append(cn_longitude)
test["Geolocate"]=Geolocate
test["Latitude"]=Latitude
test["Longitude"]=Longitude
country_info=test
In [24]:
movie=netflix[netflix["type"]=="Movie"]
tvShow=netflix[netflix["type"]!="Movie"]
In [25]:
netflix["date_added"]=pd.to_datetime(netflix["date_added"])
netflix["release_year"]=pd.to_datetime(netflix["release_year"],format='%Y')
netflix["TimeForAdd"]=netflix["date_added"]-netflix["release_year"]
netflix["TimeForAdd"]=netflix["TimeForAdd"].map(lambda x : x.days)
netflix["TimeForAdd"]=netflix["TimeForAdd"].map(lambda x : int(x))
netflix.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8790 entries, 0 to 8789
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   show_id       8790 non-null   object        
 1   type          8790 non-null   object        
 2   title         8790 non-null   object        
 3   director      8790 non-null   object        
 4   country       8790 non-null   object        
 5   date_added    8790 non-null   datetime64[ns]
 6   release_year  8790 non-null   datetime64[ns]
 7   score         8405 non-null   float64       
 8   rating        8790 non-null   object        
 9   duration      8790 non-null   object        
 10  listed_in     8790 non-null   object        
 11  Genre1        8790 non-null   object        
 12  Genre2        8790 non-null   object        
 13  Genre3        8790 non-null   object        
 14  TimeForAdd    8790 non-null   int64         
dtypes: datetime64[ns](2), float64(1), int64(1), object(11)
memory usage: 1.0+ MB
In [37]:
data=netflix.groupby(by="date_added").mean()
day=list(data["TimeForAdd"])
date=data.index
test=pd.DataFrame(date)
test["day"]=day
test["year"]=test["day"].map(lambda x:x/365)
test["year_added"]=test["date_added"].map(lambda x:x.year)
test=pd.DataFrame(test.groupby(by="year_added").mean())
plt.bar(test.index,test["year"])
plt.title("Time between release and addition to Netflix")
plt.xlabel("Year")
plt.ylabel("Average time in years")
plt.show()

C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\373988114.py:1: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.

C:\Users\adaml\AppData\Local\Temp\ipykernel_9176\373988114.py:8: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
In [27]:
df = px.data.gapminder().query("year==2007")
country=list(df["country"].unique())
list_country=list(country_info["CountryName"])
list_country[0]
Out[27]:
'United States'
In [28]:
import jaro
def get_Ressemblance_Unité(ingredient,Aliment):
    ind_max=0
    ingredient1=str(ingredient)
    score_max=0
    for i in range(len(Aliment)):
        ingredient2=str(Aliment[i])
        score=jaro.jaro_metric(ingredient1,ingredient2)
        if score >=score_max :
            ind_max=i
            score_max=score
    return Aliment[ind_max]
In [29]:
country_info["match_country"]=country_info["CountryName"].map(lambda x:get_Ressemblance_Unité(x,country))
country_info
Out[29]:
CountryName nbr_show Country Continent codes Geolocate Latitude Longitude match_country
0 United States 3240 US NA (US, NA) (39.7837304, -100.445882) 39.783730 -100.445882 United States
1 India 1057 IN AS (IN, AS) (22.3511148, 78.6677428) 22.351115 78.667743 India
2 United Kingdom 638 GB EU (GB, EU) (54.7023545, -3.2765753) 54.702354 -3.276575 United Kingdom
3 Pakistan 421 PK AS (PK, AS) (30.3308401, 71.247499) 30.330840 71.247499 Pakistan
4 Canada 271 CA NA (CA, NA) (61.0666922, -107.991707) 61.066692 -107.991707 Canada
... ... ... ... ... ... ... ... ... ...
81 Soviet Union 1 Unknown Unknown (Unknown, Unknown) (44.566633350000004, 27.359792969682104) 44.566633 27.359793 Slovenia
82 Paraguay 1 PY SA (PY, SA) (-23.3165935, -58.1693445) -23.316593 -58.169345 Paraguay
83 Puerto Rico 1 PR NA (PR, NA) (18.2247706, -66.4858295) 18.224771 -66.485829 Puerto Rico
84 Somalia 1 SO AF (SO, AF) (8.3676771, 49.083416) 8.367677 49.083416 Somalia
85 Zimbabwe 1 ZW AF (ZW, AF) (-18.4554963, 29.7468414) -18.455496 29.746841 Zimbabwe

86 rows × 9 columns

In [30]:
df.columns=["match_country", 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
       'iso_alpha', 'iso_num']
df
Out[30]:
match_country continent year lifeExp pop gdpPercap iso_alpha iso_num
11 Afghanistan Asia 2007 43.828 31889923 974.580338 AFG 4
23 Albania Europe 2007 76.423 3600523 5937.029526 ALB 8
35 Algeria Africa 2007 72.301 33333216 6223.367465 DZA 12
47 Angola Africa 2007 42.731 12420476 4797.231267 AGO 24
59 Argentina Americas 2007 75.320 40301927 12779.379640 ARG 32
... ... ... ... ... ... ... ... ...
1655 Vietnam Asia 2007 74.249 85262356 2441.576404 VNM 704
1667 West Bank and Gaza Asia 2007 73.422 4018332 3025.349798 PSE 275
1679 Yemen, Rep. Asia 2007 62.698 22211743 2280.769906 YEM 887
1691 Zambia Africa 2007 42.384 11746035 1271.211593 ZMB 894
1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE 716

142 rows × 8 columns

In [31]:
finalmap=df.merge(country_info , on="match_country")
finalmap
Out[31]:
match_country continent year lifeExp pop gdpPercap iso_alpha iso_num CountryName nbr_show Country Continent codes Geolocate Latitude Longitude
0 Argentina Americas 2007 75.320 40301927 12779.379640 ARG 32 Argentina 76 AR SA (AR, SA) (-34.9964963, -64.9672817) -34.996496 -64.967282
1 Australia Oceania 2007 81.235 20434176 34435.367440 AUS 36 Australia 114 AU OC (AU, OC) (-24.7761086, 134.755) -24.776109 134.755000
2 Austria Europe 2007 79.829 8199783 36126.492700 AUT 40 Russia 19 RU EU (RU, EU) (64.6863136, 97.7453061) 64.686314 97.745306
3 Austria Europe 2007 79.829 8199783 36126.492700 AUT 40 Austria 9 AT EU (AT, EU) (47.59397, 14.12456) 47.593970 14.124560
4 Bangladesh Asia 2007 64.062 150448339 1391.253792 BGD 50 Bangladesh 3 BD AS (BD, AS) (24.4769288, 90.2934413) 24.476929 90.293441
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
81 United States Americas 2007 78.242 301139947 42951.653090 USA 840 United Arab Emirates 21 AE AS (AE, AS) (24.0002488, 53.9994829) 24.000249 53.999483
82 Uruguay Americas 2007 76.384 3447496 10611.462990 URY 858 Uruguay 9 UY SA (UY, SA) (-32.8755548, -56.0201525) -32.875555 -56.020153
83 Venezuela Americas 2007 73.747 26084662 11415.805690 VEN 862 Venezuela 2 VE SA (VE, SA) (8.0018709, -66.1109318) 8.001871 -66.110932
84 Vietnam Asia 2007 74.249 85262356 2441.576404 VNM 704 Vietnam 7 VN AS (VN, AS) (15.9266657, 107.9650855) 15.926666 107.965086
85 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE 716 Zimbabwe 1 ZW AF (ZW, AF) (-18.4554963, 29.7468414) -18.455496 29.746841

86 rows × 16 columns

In [32]:
fig = px.choropleth(finalmap, locations="iso_alpha",
                    color="nbr_show",
                    hover_name="CountryName",
                    range_color=[0,3240],
                    center = {"lat":0,"lon":0}
                    )

fig.show()